Understanding correlation and causation and the impact in trading decisions

In statistics, correlation is a measure of the relationship between two variables. Correlations can be positive, negative, or zero. A positive correlation means that as one variable increases, the other variable also increases. A negative correlation means that as one variable increases, the other decreases. A zero correlation means that there is no relationship between the two variables.

Correlation and causation are two important concepts that traders and investors need to understand in order to make informed decisions. Simply put, correlation is a measure of how two things relate to each other, while causation is the reason why an event occurred. For example, let's say that you observe that the stock market tends to go up when the weather is sunny. This is a correlation. However, it doesn't necessarily mean that the weather causes the stock market to go up. There could be other factors at play, such as the fact that people tend to be in a good mood when it's sunny out, which could lead them to be positive about the market. Therefore, it's important to be able to distinguish between correlation and causation so that you can correctly interpret data and make informed trading and investing decisions.

Investors are always looking for patterns in the market so they can make better-informed decisions about where to invest their money. However, one should be aware of the dangers of mistaking correlation for causation. It is important to note that just because two variables are correlated does not necessarily mean that one causes the other. This is known as a spurious correlation. Spurious correlation is a type of error that can occur when analyzing data, particularly when working with large data sets. It occurs when two variables are incorrectly correlated, either due to chance or due to the presence of a third variable. This can lead to inaccurate conclusions being drawn about the relationship between the two variables. For example, there might be a positive correlation between the number of ice cream sales and the number of swimming pool drownings. But this doesn't mean that eating ice cream causes people to drown! The true cause of this spurious correlation is actually the summer weather; both ice cream sales and swimming pool drownings increase when it's hot outside.  In trading, spurious correlation could lead an analyst to conclude that there is a relationship between the number of times a company's stock price has fallen and the number of times it has been mentioned in the media. However, this conclusion would be inaccurate if the media coverage was itself caused by the stock price falls. In the world of trading and investing, spurious correlation can lead to costly mistakes if not accounted for.

So how can you tell if a correlation is real or spurious?

One way is to look at the direction of the relationship. If one variable increases while the other decreases, it's more likely to be a real correlation. For example, there is a clear negative correlation between smoking and life expectancy; as smoking increases, life expectancy decreases. It's very unlikely that this relationship is spurious; it's much more likely that smoking causes a decrease in life expectancy.

Another way to tell if a correlation is real or spurious is to look at the strength of the relationship. The stronger the correlation, the less likely it is to be spurious. For example, there might be a weak positive correlation between watching television and eating junk food. This could be spurious; maybe people who watch a lot of TV are more likely to have unhealthy lifestyles in general, which would lead to higher junk food consumption. Or maybe people who eat junk food are more likely to watch TV because they're not getting enough exercise. It's hard to say for sure without further investigation. However, if there was a strong positive correlation between watching television and eating junk food - say, for every extra hour of TV watched, people ate an extra pound of junk food - then it's much less likely that this relationship is spurious. In this case, it's more likely that TV really does cause people to eat more junk food!

But the most important way to avoid spurious correlations is to build domain knowledge about how the market works as then traders can filter out relationships that do not make logical sense.  Our co-founder and CEO, Ruban Phukan, has written a foundational book for the next generation of the stock market, forex, and crypto traders on how markets work and how to approach the market in a systematic way. You can read more here: https://blog.researchfin.ai/posts/the-foundational-book-on-systematic-trading